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What happens when models ignore the order of events? In this talk, Terry Lyons explains why multimodal streamed data must be treated differently than unimodal data—and how traditional models fall short by overlooking timing and relationships. Using simple examples—like whether a commuter catches a bus—Lyons shows how Rough Path Theory captures essential order information, enabling a more accurate and efficient description of data streams. Learn how this framework: Preserves event order without dense sampling Reduces data dimensionality and training set size Powers real-world applications with massive impact A must-watch for anyone working in data science, machine learning, or mathematics. 🔔 Subscribe for more insights on the future of data modeling. Keep up-to-date on SIAM/BFS Webinars at https://wiki.siam.org/siag-fm/index.p... Watch previous SIAM FME webinars at • SIAM Activity Group on FME Virtual Talk Se... Learn more about SIAM Activity Group on Financial Mathematics and Engineering at https://www.siam.org/get-involved/con... #DataScience #RoughPathTheory #TerryLyons #StreamingData #TimeSeries #AI #Mathematics